The PhD Program in Engineering Cybernetics
The Department of Engineering Cybernetics participates in a number of research projects and can offer many exciting research challenges for PhD candidates. The department conducts both basic and industry-oriented research. Consequently, PhD candidates can work on both practical and theoretical problems often in close cooperation with university scholars and leading Norwegian companies.
The PhD-program corresponds to 3 years full time study, but the position can be extended to 4 years, including a 25 percent work requirement as teaching assistant (TA). Applicants must have knowledge equivalent to Master of Technology.
The program is intended for people who want a career in higher education and/or work with research in companies and research institutes. The program consists of two parts: a training component for 30 credits and a thesis with independent research work of 150 credits. The plan for the PhD program is designed in consultation with the candidate and supervisor. The department has many good contacts in national and international research communities.
The doctoral education at the department is characterized by high quantity and high quality. Since 2000, PhD candidates from the department have four times received the ESSO Award for best doctoral dissertation in either basic or applied research at NTNU. Many PhD candidates have received the best paper award at international conferences and for publications in high quality journals.
The department offers the following PhD courses:
TK8102 Nonlinear Observer Design
TK8103 Advanced Nonlinear Systems
TK8105 Ultrasound imaging in Heterogeneous, Non-Linear Tissue
TK8107 Estimation in Nonlinear Systems
TK8108 Topics in Fisheries and Aquaculture Cybernetics for PhD students
TK8109 Advanced Topics in Guidance and Navigation
TK8110 PhD Seminar in Estimation and Data Fusion
TK8111 Topics in System and Control Theory
TK8112 The Theory of Concurrency in Real-Time Systems
TK8114 PhD Seminar in Industrial Computing
TK8115 Numerical optimal control